Underwater vision is plagued by poor visibility conditions. Direct employment of most computer vision methods (e.g., those based on stereo triangulation or on structure from motion) underwater is difficult. This is due to the particularly challenging environmental conditions, which complicate image matching and analysis. It is important to alleviate these visibility problems, since underwater imaging is widely used in scientific research and technology. Computer vision methods are being used in this mode of imaging for various applications (see, for example, A. Ortiz, M. Simo, and G. Oliver, “A vision system for an underwater cable tracker,” in Machine Vision and Applications, vol. 13, pp. 129-140, 2002) such as mine detection, inspection of underwater power and telecommunication cables, pipelines, nuclear reactors, and columns of offshore platforms. Underwater computer vision is also used commercially to help swimming pool life-guards. As in conventional computer vision, algorithms are sought for navigation and control of submerged robots. In addition, underwater imaging is used for research in marine biology, archaeology and mapping. Moreover, underwater photography is becoming more accessible to the wider public.
What makes underwater imaging so problematic? Underwater, visibility degradation-effects vary as distances to the objects increase. Since objects in the field of view (FOV) are at different distances from the camera, the causes for image degradation are spatially varying. Contrary to this fact, traditional image enhancement tools, e.g., high pass filtering and histogram equalization are typically spatially invariant. Since they do not model the spatially varying distance dependencies, traditional methods are of limited utility in countering visibility problems.
A common approach to improve underwater visibility and color is based on artificial illumination. The most popular realization of this approach (see, for example, B. Skerry and H. Hall, Successful Underwater Photography. New York: Amphoto books, 2002, pp. 25-41) uses an off axis wide-field strobe attached to the camera. A significant problem associated with this is the falloff of scene irradiance with the distance from the strobe. Moreover, “sea snow” may be created by the defocus blur of the strong backscatter from suspended particles at close distances.
To bypass the backscatter problem, advanced research underwater imaging systems use specialized active radiation hardware (see, for example, G. D. Gilbert and J. C. Pernicka, “Improvement of underwater visibility by reduction of backscatter with a circular polarization technique,” App. Opt., vol. 6, pp. 741-746, 1967). Yet, the range of such systems is limited, for the reason mentioned with respect to a wide field torch: at some distance the source's falloff leads to too low levels of scene irradiance. Such systems tend to be highly power consuming, complex and expensive. These problems are avoided by passive computer vision which exploits natural illumination. When available, natural illumination exists all over the scene, alleviating the need to project energy towards objects.
It was demonstrated decades ago (J. N. Lythgoe and C. C. Hemmings, “Polarized light and underwater vision,” Nature, vol. 213, pp. 893-894, 1967) that polarization filtering can enhance contrast in passive underwater vision. Yet, using the raw result of simple optical filtering may have a limited effect, indicating that some post processing is needed, based on acquisition of both components of polarized light. One approach is based on a simple subtraction of the differently polarization filtered images (see, for example, L. J. Denes, M. Gottlieb, B. Kaminsky, and P. Metes, “AOTF polarization difference imaging,” in Proc. SPIE Advances in Computer-Assisted Recognition, vol. 3584, 1999, pp. 106-115) or displays the degree of polarization (DOP) (for example, M. P. Rowe, E. N. Pugh, Jr., J. S. Tyo, and N. Engheta, “Polarization-difference imaging: a biologically inspired technique for observation through scattering media,” Optics Letters, vol. 20, pp. 608-610, 1995). That approach has fundamental disadvantages. It assumes that polarization is associated with the object radiation, rather than the causes for the degradation of this signal. However, due to depolarization, that assumption becomes invalid as distances increase.
Our approach is based on physics-based model that accounts for degradation effects that depends of the object distance (from the camera and the light source), and on optical phenomena that are associated with these effects. In particular, we exploit the fact polarization can be associated with the prime visibility disturbance, which we wish to remove (veiling light). The association of polarization to veiling light has recently been utilized in an attempt to enhance visibility in a limited setting where illumination was perpendicular to the line of sight (LOS) (see P. C. Y. Chang, J. C. Flitton, K. I. Hopcraft, E. Jakeman, D. L. Jordan, and J. G. Walker, “Improving visibility depth in passive underwater imaging by use of polarization,” App. Opt, vol. 42, pp. 2794-2802, 2003). That method used ad-hoc processing. Such enhancement methods are far from inverting the image formation process and recovering the objects. In contrast, our approach inverts the general physical model, thus the recovered image is similar to clear visibility appearance.
It is a purpose of the present invention to provide a novel method and system for enhancing underwater imaging.